The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments to investigate the relationship between the nitrogen content and spectral reflectance of apple canopy leaves. Two different preprocessing methods, Savitzky–Golay (SG) smoothing and multiple scattering correction (MSC), were used to extract the feature bands by combining the successive projection method (SPA) and the competitive adaptive weighting algorithm–partial least squares (CARS-PLS). The reflectance values of the feature bands screened via these two methods were used as inputs to construct the multi-factor inversion models of apple canopy leaf nitrogen content based on the long- and short-term memory (LSTM) network, the support vector regression (SVR) and the Least Squares Support Vector Machine Regression (RIME-LSSVM). The study compared the ability of three algorithmic models to estimate leaf nitrogen content, and the results showed that the model constructed with the reflectance values of the characteristic bands screened by the CARS-PLS algorithm as inputs was more effective in predicting the nitrogen content of leaves. Furthermore, the accuracy of the model constructed using RIME-LSSVM was significantly higher than that of the model constructed using the long- and short-term memory network and support vector regression, in which the coefficient of determination of the test set (R-squared) is 0.964 and the root-mean-squared error (RMSE) is 0.052. Finally, the CARS-PLS algorithm combined with the RIME-LSSVM model has a higher prediction accuracy. The study demonstrated the feasibility and reliability of hyperspectral techniques for the estimation of nitrogen content of apple leaves in the Aksu region.

Fractional Vegetation Cover (FVC) is an important indicator to evaluate the quality of the regional ecological environment. Alar City is a typical desert oasis region. Investigating the spatial and temporal changes in its vegetation cover at different stages is a guide to the ecological balance and sustainable green development of the Tarim River basin. Based on the Google Earth Engine (GEE) cloud platform, this study analyzed the spatial and temporal characteristics and trends of vegetation cover changes in Alar City from 1990 to 2020 using the Hurst index and coefficient of variation. The results show that the spatial distribution of vegetation in the study area in the last 30 years shows a wave-like characteristic with an overall apparent upward trend. The vegetation cover in the study area is predominantly increasing and the spatial distribution shows a phased and regional character. Compared with 1990, there is a significant increase in the area of cultivated land in 2020. Among them, the areas of vegetation growth mainly occur in the basin around the Tarim River. Human activities have weakened the influence of natural factors on FVC. The results of the study suggest that the GEE platform can be an effective tool for permanently monitoring vegetation.

Modern agriculture requires more accurate field management capability compared with traditional agriculture. The development of hyperspectral remote sensing technology embodies rapid and non-destructive features in agricultural information monitoring, providing a technical guarantee for the scientific management of agricultural production. The mathematical model of inverse cotton leaf total nitrogen was established by decomposing and transforming the original cotton leaf spectrum using continuous wavelet analysis and traditional spectral transformation, taking the characteristic wavelet coefficients and spectral characteristic bands as independent variables, and using univariate, stepwise regression, and partial least squares methods. The correlation between the total nitrogen content of cotton leaves and the spectral reflectance, through different methods of spectral treatment, was improved to different degrees. For the conventional spectral transformation, the inverse logarithmic first-order differential lg′(1/R) improved the correlation of cotton leaf total nitrogen by 0.26. The continuous wavelet analysis outperformed the conventional spectral model regarding information noise reduction and mining of feature information. The established model with RPD>2 had good stability and predictive power for all sample data.

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years.

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

Red jujube is one of the most important crops in China. In order to meet the needs of the scientific and technological development of the jujube industry, solve the problem of poverty, realize the backward advantage, and promote economic development, smart agriculture is essential. The main objective of this study was to conduct an online detection study of unpicked red jujubes in order to detect as many red jujubes in the picture as possible while minimizing the occurrence of overfitting and underfitting. Experiments were conducted using the Histogram of Oriented Gradients + Support Vector Machine (HOG+SVM) traditional detection method and the You Only Look Once version 5 (YOLOV5) and Faster R-CNN modern deep learning detection methods. The precision, recall, and F1 score were compared to obtain a better algorithm. The study also introduced the AlexNet model with the main objective of attempting to combine it with other traditional algorithms to maximize accuracy. Labeling was used to label the training images in YOLOV5 and Faster Regions with CNN Features (Faster R-CNN) to train the machine model so that the computer recognized these features when it saw new unlabeled data in subsequent experiments. The experimental results show that in the online recognition detection of red jujubes, the YOLOV5 and Faster R-CNN algorithms performed better than the HOG + SVM algorithm, which presents precision, recall, and F1 score values of 93.55%, 82.79%, and 87.84% respectively; although the HOG + SVM algorithm was relatively quicker to perform. The precision of detection was obviously more important than the efficiency of detection in this study, so the YOLOV5 and Faster R-CNN algorithms were better than the HOG + SVM algorithm. In the experiments, the Faster R-CNN algorithm had 100% precision, 99.65% recall, an F1 score of 99.82%, and 83% non-underfitting images for the recognized images, all of which were higher than YOLOV5′s values, with 97.17% recall, an F1 score of 98.56%, and 64.42% non-underfitting. In this study, therefore, the Faster R-CNN algorithm works best.